HEGrid：异构计算环境下高效的多通道射电天文数据网格框架
HEGrid: A High Efficient Multi-Channel Radio Astronomical Data Gridding Framework in Heterogeneous Computing Environments
摘要：
充分利用现有和即将推出的大型单碟射电望远镜等科学仪器的潜力所面临的挑战是有效和高效地处理收集到的大量数据。作为一种具有“摩尔邻域模式”的“准2D模板计算”，网格化是射电天文学研究数据简化管道中计算量最大的步骤，使天文学家能够创建正确的天空图像以供进一步分析。然而，现有的网格框架要么只能运行在多核CPU架构上，要么不支持高并发、多通道的数据网格。它们的性能是有限的，并且需要创新的网格框架来处理来自大型单碟射电望远镜（如500米孔径球面望远镜（FAST））的数据。为了应对这些挑战，我们开发了一个高效网格框架HEGrid，克服了上述限制。具体而言，我们提出并构建了网格流水线在异构计算环境中，并实现了高性能的多通道处理的多管道并发。此外，我们提出了基于流水线的协同优化，以减轻潜在的负面性能影响可能的内部和内部流水线低计算和I/O利用率，包括组件共享为基础的冗余消除，线程级的数据重用和重叠的I/O和计算。我们的实验是基于模拟数据集和实际FAST观测数据集。结果表明，HEGrid的性能比其他最先进的网格框架高出5.5倍，并具有强大的硬件可移植性，包括AMD Radeon Instinct GPU和NVIDIA GPU。
英文摘要：
The challenge to fully exploit the potential of existing and upcoming scientific instruments like large single-dish radio telescopes is to process the collected massive data effectively and efficiently. As a "quasi 2D stencil computation" with the "Moore neighborhood pattern," gridding is the most computationally intensive step in data reduction pipeline for radio astronomy studies, enabling astronomers to create correct sky images for further analysis. However, the existing gridding frameworks can either only run on multi-core CPU architecture or do not support high-concurrency, multi-channel data gridding. Their performance is then limited, and there are emerging needs for innovative gridding frameworks to process data from large single-dish radio telescopes like the Five-hundred-meter Aperture Spherical Telescope (FAST). To address those challenges, we developed a High Efficient Gridding framework, HEGrid, by overcoming the above limitations. Specifically, we propose and construct the gridding pipeline in heterogeneous computing environments and achieve multi-pipeline concurrency for high performance multi-channel processing. Furthermore, we propose pipeline-based co-optimization to alleviate the potential negative performance impact of possible intra- and inter-pipeline low computation and I/O utilization, including component share-based redundancy elimination, thread-level data reuse and overlapping I/O and computation. Our experiments are based on both simulated datasets and actual FAST observational datasets. The results show that HEGrid outperforms other state-of-the-art gridding frameworks by up to 5.5x and has robust hardware portability, including AMD Radeon Instinct GPU and NVIDIA GPU.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
